Newsvendor Research Articles

Inventory Decisions Considering Returns in Omni-channel Retailing

Omni-channel retail is a new model that is achieved by integrating online stores and offline physical stores. The inventory decision problem considering returns under omni-channel retail is studied in two cases: considering same-channel returns under omni-channel retail and considering cross-channel returns under omni-channel retail. Based on the consideration of consumer returns and resale of returned products, the newsboy model is used to construct inventory decision models with the goal of maximizing retailer profits and analyze the models. Numerical analysis shows that the online store return ratio and the cross-channel return ratio have a significant impact on the inventory and profit of the retailer's offline physical stores and online stores. In particular, when the cross-selling profit is large, the cross-channel return ratio increases, and the retailer's profit increases accordingly, but the retailer's profit may decrease with the increase of the online store return ratio.

Thinking orientation and overconfidence: a newsvendor study

This study uses the newsvendor problem to investigate the correlations between thinking orientation, overconfidence, and economic outcome. We aim to shed light on possible interconnections between these variables and extend the existing conceptual models of thinking orientation and overconfidence. To test the conceptual model and the corresponding hypotheses empirically, we employed a laboratory experiment with 50 hypothetical decision periods in which 142 participants ordered a highly profitable product. We found compelling evidence that suggests overprecision as the mediating variable between thinking orientation and the economic outcome. Furthermore, this research offers some practical implications.

The tradeoff between maximizing expected profit and minimizing the maximum regret in the newsvendor problem

The tradeoff between maximizing expected profit and minimizing the maximum regret in the newsvendor problem

Empirical risk minimization for big data driven prescriptive analytics: An exploration of two-stage stochastic programs with recourse

In the operations research literature, data driven analyses using big data are receiving more and more interest and attention. However, big data driven operational analyses are still limited in scope, with only unconstrained big data driven problems, e.g. the newsvendor problem, being comprehensively explored. If big data driven analyses are to become a core part of operations research and practice, one must be able to formulate and solve constrained big data driven models. We therefore propose a direct empirical risk minimization (DERM) method for formulating and solving a class of constrained big data driven operation research problems. In big data driven problems, relevant operational prescriptions are dependent on a set of observed features. In this paper, we assume linear feature relationships, which enable us to fundamentally base the DERM method on linear stochastic programming. The assumption of linear feature relationships is also seen in the literature on big data driven newsvendor models. We prove that if a big data driven solution exists, the DERM method will always, for the class of constrained problems we study, arrive at a feasible solution of the initial operational problem. Moreover, we exemplify the DERM method by formulating and solving two specific big data driven operational problems. In the numerical study of the two operational problems, we show, that under linear demand, the DERM method outperforms, with regard to cost, two benchmark methods and a big data driven method from literature.

Distributionally robust optimization of a newsvendor model under capital constraint and risk aversion

This paper proposes a capital-constrained newsvendor model with risk aversion under a partially known demand distribution with only knowledge of its mean and variance. We adopt the robust conditional value-at-risk (RCVaR) to characterize the vendor’s risk aversion. Firstly, we obtain the closed-form RCVaR optimal order quantity that depends on the demand volatility level: When demand volatility is low, the vendor has four financing-ordering strategies contingent upon different capital levels. When demand volatility is medium, the vendor does not seek bank loans and is left with two ordering strategies. When demand volatility is high, the vendor does not bother placing an order at all. Then, we investigate the impact of capital constraint, risk aversion and demand volatility on the RCVaR optimal order quantity. Finally, we demonstrate the robustness of the RCVaR optimal ordering policy by numerical experiments based on both randomly generated and real-world data.

Robust concave utility maximization over chance constraints

This paper first studies an expected utility problem with chance constraints and incomplete information on a decision maker’s utility function. The model maximizes the worst-case expected utility of random outcome over a set of concave functions within a novel ambiguity set, while the underlying probability distribution is known with the assumption of a discretization of possible realizations for the uncertainty parameter. To obtain computationally tractable formulations, we employ a discretization approach to derive a max–min chance-constrained approximation of this problem. This approximation is further reformulated as a mixed-integer program. We show that the discrete approximation converges to the true counterpart under mild assumptions. We also present a row generation algorithm for optimizing the max–min program. A computational study for a bin-packing problem and a multi-item newsvendor problem is conducted to demonstrate the benefit of the proposed framework and the computational efficiency of our algorithm. We find that the row generation algorithm can significantly reduce the computational time, and our robust policy could achieve a better out-of-sample performance when compared with the non-robust policy and the one without the chance constraints.

Research on Topology Optimization of Building Digital Supply Chain Based on Genetic Neural Network

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

OPTIMAL DECISION-MAKING FOR NEWSPAPER INVENTORY: BALANCING DEMAND UNCERTAINTY AND PROFITABILITY

Estimating cost-benefit analysis, calculating the real value of anything before buying it is essential in order to know if this purchase is fetching or not. In the industry of selling newspapers, since demand is determined by various unpredictable features like news quality and types of reader interest, it may be difficult to estimate how many copies you should buy daily. On the other hand, this study addresses the classic Newsvendor Problem by analyzing a single-period inventory management model to find the optimal order quantity that maximizes profit. Simulating different demand scenarios up to 20 days into the future, using financial-based assumptions (e.g.: selling price, buying cost, and salvage value) on order quantities in order to investigate profits. The results show that we can identify the optimal level of order quantity in the cost-benefit analysis and profit analysis under different demand situations. This method reduces both excess waste and missed sales opportunities, making an optimal answer for inventory alignment in sought-after uncertainty-based industries. The insights of this analysis are not just relevant to newspaper sales but also to all comparable industries where accurate inventory management becomes challenging.

Voyage charterparty arrangement for river tramp shipping: Green and traditional vessels comparison

Voyage charterparty arrangement (VCA) for river shipping is a common agreement between a shipowner and a charterer regarding the transportation of specific cargoes from one port to another via a tramp vessel. Optimizing VCA with the essential terms such as laytime and sailing speed of the vessel, is a crucial task for both parties to reduce potential disputes and achieve win-win outcomes. This paper models the problem from two perspectives: the VCA negotiation stage and execution stage. Firstly, according to the given probability distribution of port time, the theoretically optimal laytime is obtained by using textbook formulation of newsvendor problems (NVP). On this basis, considering the concerns about the inaccuracy of the given probability distribution and the adjustment measures taken by charterers and shipowners in reality, an integrated adjustment model is constructed to determine the optimal laytime in the VCA negotiation stage. Secondly, taking into account the vessel's uncertain lock crossing time, an operations coordination model with information sharing of the berth plan is established to obtain the optimal sailing speed under a certain confidence level in the VCA execution stage. Finally, a case study of chartering tramp vessels on the Yangtze River validates the applicability and effectiveness of the models proposed in this paper, and some important management insights are derived. The results show that establishing lower optimal speed limits and relatively shorter laytime when signing a contract with the shipowner of green river vessels is reasonable for the charterer. By notifying the shipowner of the berth plan after the green river vessel crosses through the lock, the charterer can avoid higher demurrage costs due to speeding-up. Operations coordination would be mutually beneficial without abusing the contract terms. Compared to traditional river vessels, it's more reasonable to reduce the benefits allocated to charterers from the deceleration of green river vessels by shipowners.

A newsvendor model with multiple reference points: Target-setting for aspirational newsvendors

Prospect theory posits that the determination of an outcome as a gain or loss hinges upon the reference points, thereby exerting a substantial influence on the decision-making processes of individuals. These reference points can encompass both external targets and internal aspirations (self-goals), forming two potential candidates. Despite a growing body of evidence showcasing the concurrent impact of multiple reference points on decision-makers’ choices, scant attention has been accorded to this phenomenon within the realm of operations management literature. In light of this, the present study delves into the influence wielded by an external target, functioning as a reference point, on the decisions and profitability of newsvendors who also harbor a well-defined aspiration (self-goal) reference point. The study introduces two distinct archetypes of newsvendors: the balanced newsvendors, adept at harmonizing personal needs with target attainment and thus navigating decisions influenced by dual reference points; and the focused newsvendors, singularly fixated on target realization and thereby governed by a sole target reference point. Our findings reveal that the effects of target-setting in boosting expected profits on both categories of newsvendors are contingent upon the specific value attributed to their self-goal. The implications of target-setting can either yield advantages or drawbacks, contingent upon the self-goal. This asymmetry in outcomes holds true for both balanced and focused newsvendors, with the former exhibiting a higher likelihood of deriving benefits from target-setting. Moreover, an exploration of scenarios wherein a newsvendor’s self-goal may undergo updates underscores the potential for further enhancement in the performance of balanced newsvendors.

Robust ordering, production, and replenishment in a supply chain for innovative products: A two-echelon newsvendor problem with partial information in yield and demand

With partial yield and demand information, we investigate a two-echelon newsvendor problem in a supply chain for innovative product, in which the retailer and the supplier make sequential decisions in ordering, production, and replenishment, respectively. We adopt the minimax-regret criterion to assume the risk-averse nature and deduce the minimax-regret inventory decisions. We explore replenishment (emergency) production strategies for the supplier. Then we propose shortage penalty strategies as well as subsidized replenishment strategies for the retailer so that the supplier can maintain the required supply even under higher replenishment production costs. We further reveal the role of information uncertainty in the minimax-regret decisions and the bullwhip effect. Interestingly, the results show that the penalty strategies do not always lead to less regret for the retailer, and the information uncertainty does not necessarily lead to greater regret for the supplier. Moreover, some schemes to reduce the bullwhip effect are proposed, such as improving the accuracy of uncertain yield, increasing shortage penalty or replenishment subsidy, and decreasing replenishment cost. It is verified that the replenishment strategies can increase profits for both the retailer and the supplier under some conditions. In extensions, we reveal the supplier’s preference for emergency production and external procurement, as well as preferences for information sharing and confidentiality. Meanwhile, we find that decentralized decision-making may even outperform centralized decision-making in some situations when the minimax-regret criterion is adopted to deal with the information deficiency. Finally, the robustness of the replenishment strategies in reducing both enterprises’ regrets is checked.

Newsvendor problem with discrete demand and constrained first moment under ambiguity

Newsvendor problem with discrete demand and constrained first moment under ambiguity

Distributionally Robust Newsvendor Under Stochastic Dominance with a Feature-Based Application

Problem definition: In this paper, we study the newsvendor problem under some distributional ambiguity sets and explore their relations. Additionally, we explore the benefits of implementing this robust solution in the feature-based newsvendor problem. Methodology and results: We propose a new type of discrepancy-based ambiguity set, the JW ambiguity set, and analyze it within the framework of first-order stochastic dominance. We show that the distributionally robust optimization (DRO) problem with this ambiguity set admits a closed-form solution for the newsvendor loss. This result also implies that the newsvendor problem under the well-known infinity-Wasserstein ambiguity set and Lévy ball ambiguity set admit closed-form inventory levels as a by-product. In the application of feature-based newsvendor, we adopt general kernel methods to estimate the conditional demand distribution and apply our proposed DRO solutions to account for the estimation error. Managerial implications: The closed-form solutions enable an efficient computation of optimal inventory levels. In addition, we explore the property of optimal robust inventory levels with respect to the nonrobust version via concepts of perceived critical ratio and mean repulsion. The results of numerical experiments and the case study indicate that the proposed model outperforms other state-of-the-art approaches, particularly in environments where demand is influenced by covariates and difficult to estimate. Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001, 24-0500-A0001] and National Natural Science Foundation of China [Grant 72331004]. L. Zhang is partially supported by the National Natural Science Foundation of China [Grants 72171156 and 72231002] and the Hong Kong Research Grants Council [Grant 16212419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0159 .

Time series imaging-based deep learning method for inventory control

ABSTRACT Data-driven newsvendor methods offer promising solutions for estimating replenishment quantity within complex markets by leveraging demand-related features. However, most features utilized by these methods rely on manual selection, and some important unobservable features cannot be thoroughly utilized. These issues might result in the loss of some crucial information, thus incurring considerable cost in these decision-making results, meaning that there is still much room for improvement. To remedy this, we propose a time series imaging-based deep learning method, which automatically extracts crucial features alongside those selected manually. This method maximizes the advantages of time series imaging, convolutional neural networks, bidirectional long short-term memory networks, and multilayer perceptron networks. Experiments with four diverse product time series and real-world data show that our approach reduces the average newsvendor problem cost by 16.33% to 33.43% compared to the best benchmark, highlighting its robustness. Additionally, each component within our method significantly contributes to decision-making.

Meta-inventory management decisions: A theoretical model

The manufacturing industry leverages the traceability and visibility of Industrial 4.0 to integrate digital and physical twins for solving the intricacy of a production system. Physical entities can be converted into digital twins with smart IoT (Internet of Things) devices and computational techniques. Research efforts have generally focused on the development of digital twin models for basic practical applications. Advanced applications of digital twins have not been widely reported. An innovative use of digital twins for inventory management has only been analysed theoretically and reported as meta-inventory management for the first time by Wang and Huang (2023). This paper extends the novel concept of meta-inventory by using a theoretical Newsvendor model for original equipment manufacturing (OEM) and own brand manufacturing (OBM) factories. The impacts of meta-inventory on both supply chain members, including the factory and its downstream retailer, are investigated through two kinds of response models (e.g., hybrid and separate response models). Analytical and numerical results show that a factory achieves better performance by using a separate model since it clarifies the responsibility of digital twins. The hybrid response model holds a higher proportion of digital inventory, but its final profits are less than that in the separate model due to fewer orders and higher prices and costs of uncertainty. OBM can better leverage the advantage of digital twins than OEM. Also, both supply chain members benefit from the implementation of meta-inventory out of profit increase, price reduction, and risk hedging. This research provides guidance for manufacturing sites to implement digital twins and reduce their concerns on investing in meta-inventory.

Supervised learning for integrated forecasting and inventory control

We explore the use of supervised learning with custom loss functions for multi-period inventory control with feature-driven demand. This method directly considers feature information such as promotions and trends to make periodic order decisions, does not require distributional assumptions on demand, and is sample efficient. The application of supervised learning in inventory control has thus far been limited to problems for which the optimal policy structure is known and takes the form of a simple decision rule, such as the newsvendor problem. We present an approximation approach to expand its use to inventory problems where the optimal policy structure is unknown. We test our approach on lost sales, perishable goods, and dual-sourcing inventory problems. It performs on par with state-of-the-art heuristics under stationary demand. It outperforms them for non-stationary perishable goods settings where demand is driven by features, and for non-stationary lost sales and dual-sourcing settings where demand is smooth and feature-driven.

Pricing strategy based on a stochastic problem with barter exchange under variable promotional effort for a retail channel

Barter exchange platform gains popularity as an effective trade exchange strategy to minimize the vast use of paper money. In retail management, the application of the barter platform is growing because of cashless trade. It allows a retail enterprise to trade unused products in exchange for other assets purchased by reducing the actual purchase of assets. In this context, the study analyzes a stochastic model of a barter exchange problem under a retail channel. It investigates the barter exchange policy in three different models: conventional newsvendor model without barter exchange, with barter exchange, and with barter exchange with promotional effort and emissions level. The market demand for products is considered random and random with deterministic variables for three types of models. Stochastic models are solved in two different ways: with and without probability distribution functions of the random market demand. For maximization of the retailer's profit, the proposed framework obtains optimal decisions on ordered quantity, retail price, promotional effort, and emissions level. Five examples are tested and numerical results show that the barter exchange policy is more profitable than a traditional newsvendor model. A combination of stochastic and deterministic variable demand provides 34.31% more profit than a stochastic demand. Sensitivity analysis, discussions, and managerial insights are provided in detail on optimal decisions.

Direct Trade Sourcing Strategies for Specialty Coffee

Problem definition: Leading specialty coffee roasters rely on direct trade to source premium coffee beans. We examine a roaster who sells two basic types of roasts: (1) a single-origin roast sourced from a specific locale and (2) a blend roast that uses a mix of beans from sources that vary over the course of a year. The prices of blend roasts are lower than those of single-origin roasts and appeal to a larger market. We study how characteristics of the operating and market environment affect the optimal sourcing strategy for single-origin beans. Methodology/results: We develop a two-stage stochastic program with recourse that reflects these characteristics. A roaster has the option to allocate some of the single-origin beans for sale under a blend label, known as downward substitution. We identify three distinct optimal sourcing strategies—specialized (no downward substitution), diversified (consistent downward substitution), and mixed (between these extremes)—and show that they are robust under different definitions of yield and demand. Managerial implications: We identify four main insights: (1) Two factors determine which strategy is optimal: the mean price of the inferior product (blend label) and the marginal cost of the superior product (single-origin label). (2) When compared with the newsvendor model, we find distinct structural differences across strategies. For example, whereas the effects of increasing uncertainty on optimal quantity align with the newsvendor model under a mixed strategy, the effects are distinctly different under specialized and diversified strategies (e.g., monotonic decreasing behavior for specialized, no change in quantity under diversified). (3) The weighted average price of an agricultural product is decreasing in negative yield–price correlation. We coin this as the “farmer’s curse,” which carries lessons for direct trade sourcing (e.g., advocating against paying the grower at postharvest market prices). (4) We find evidence of a virtuous feedback loop wherein the grower–roaster relationship becomes stronger over time. Our findings also point to a simple signal that policymakers may use to identify coffee growing locales where targeted interventions can improve grower welfare. Funding: B. Kazaz is thankful for summer support provided by the Whitman School of Management, Syracuse University. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0586 .

Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization

We consider distributionally robust optimization (DRO) problems, reformulated as distributionally robust feasibility (DRF) problems, with multiple expectation constraints. We propose a generic stochastic first-order meta-algorithm, where the decision variables and uncertain distribution parameters are each updated separately by applying stochastic first-order methods. We then specialize our results to the case of using two specific versions of stochastic mirror descent (SMD): (i) a novel approximate version of SMD to update the decision variables, and (ii) the bandit mirror descent method to update the distribution parameters in the case of [Formula: see text]-divergence sets. For this specialization, we demonstrate that the total number of iterations is independent of the dimensions of the decision variables and distribution parameters. Moreover, the cost per iteration to update both sets of variables is nearly independent of the dimension of the distribution parameters, allowing for high-dimensional ambiguity sets. Furthermore, we show that the total number of iterations of our algorithm has a logarithmic dependence on the number of constraints. Experiments on logistic regression with fairness constraints, personalized parameter selection in a social network, and the multi-item newsvendor problem verify the theoretical results and show the usefulness of the algorithm, in particular when the dimension of the distribution parameters is large. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: This work was supported by the National Science Foundation [Grant 2112533]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0167 .

Production Location Selecting and Subsequent Production Decision Making for Sport Obermeyer

This paper revolved around two core research issues: production location selection and subsequent production decisions. The aim of the study was to provide a detailed analysis of Sport Obermeyer's data from 1992 to 1995 in a new way, hoping to assist companies facing similar challenges. The paper took Cost, Quality, Lead time and Minimum order quantity as bridges to the four main factors affecting the choice of production location. It detailed the advantages and disadvantages of the two production locations: Hong Kong and China. Then, with the aid of the Newsvendor model, the mismatch cost ratio for the 10 styles was determined. Ultimately, using the coefficient of variance for comparison and confirmation of the mismatch cost ratio results, the paper helped Sport Obermeyer analyze which styles of parkas had low-risk and low-uncertainty demand, and which had high-risk and high-uncertainty. The paper then combined the characteristics of the two production locations and assisted Sport Obermeyer in making subsequent production decisions. Five styles of parkas suitable for production in China and five styles suitable for production in Hong Kong were identified, ensuring stable profits and deliveries for Sport Obermeyer.